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Identifying homogenous subgroups for individual patient meta-analysis based on Rough Set Theory
Summary
This study introduces a new method using Rough Set Theory (RST) to identify and manage heterogeneity in individual patient data meta-analyses. The approach effectively creates patient subgroups with low statistical heterogeneity, improving data reliability.
Area of Science:
- Biostatistics
- Health Informatics
Background:
- Heterogeneity in clinical trials can compromise meta-analysis validity.
- Current methods like sensitivity or subgroup analysis fail to explain heterogeneity's origins.
Purpose of the Study:
- To propose a novel methodology using Rough Set Theory (RST) for detecting, explaining, and managing heterogeneity in individual patient data (IPD) meta-analysis.
- To improve the interpretation and validity of meta-analysis results.
Main Methods:
- Utilized Rough Set Theory (RST) and its relations of discernibility and indiscernibility.
- Applied the methodology to a dataset of 1,111 patients from 9 randomized controlled trials on transplantation procedures for hematologic malignancies.
- Created homogeneous patient subgroups with significantly reduced statistical heterogeneity.
Main Results:
- Successfully identified and managed sources of heterogeneity in IPD meta-analysis.
- Generated three patient subgroups with low heterogeneity values (16.8%, 0%, and 0%).
Conclusions:
- The proposed RST-based methodology offers a standardized and automated approach for handling heterogeneity in IPD meta-analysis.
- Potential for future applications in personalized healthcare by analyzing treatment effects across different patient risk groups.
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